AI RESEARCH
High-Probability Bounds for SGD under the Polyak-Lojasiewicz Condition with Markovian Noise
arXiv CS.LG
•
ArXi:2603.14514v1 Announce Type: new We present the first uniform-in-time high-probability bound for SGD under the PL condition, where the gradient noise contains both Markovian and martingale difference components. This significantly broadens the scope of finite-time guarantees, as the PL condition arises in many machine learning and deep learning models while Markovian noise naturally arises in decentralized optimization and online system identification problems.